Technical phosphoproteomic and bioinformatic tools useful in cancer research

Integrated proteomic and phosphoproteomic analysis for characterization of colorectal cancer

Proteins and translationally modified proteins like phosphoproteins have essential regulatory roles in tumorigenesis. This study attempts to elucidate the dysregulated proteins driving colorectal cancer (CRC). To explore the differential proteins, we performed iTRAQ labeling proteomics and TMT labeling phosphoproteomics analysis of CRC tissues and adjacent non-cancerous tissues. The functions of quantified proteins were analyzed using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and Subcellular localization analysis. Depending on the results, we identified 330 differential proteins and 82 phosphoproteins in CRC. GO and KEGG analyses demonstrated that protein changes were primarily associated with regulating biological and metabolic processes through binding to other molecules. Co-expression relationships between proteomic and phosphoproteomic analysis revealed that TMC5, SMC4, SLBP, VSIG2, and NDRG2 were significantly dysregulated differential proteins. Additionally, based on the predicted co-expression proteins, we identified that the stem-loop binding protein (SLBP) was up-regulated in CRC cells and promoted the proliferation and migration of CRC. This study reports an integrated proteomic and phosphoproteomic analysis of CRC to discern the functional impact of protein alterations and provides a candidate diagnostic biomarker or therapeutic target for CRC. SIGNIFICANCE: Combining one or more high-throughput omics technologies with bioinformatics to analyze biological samples and explore the links between biomolecules and their functions can provide more comprehensive and multi-level insights for disease mechanism research. Proteomics, phosphoproteomics, metabolomics and their combined analysis play an important role in the auxiliary diagnosis, the discovery of biomarkers and novel therapeutic targets for colorectal cancer. In this integrated proteomic and phosphoproteomic analysis, we identified proteins and phosphoproteins in colorectal cancer tissue and analyzed potential mechanisms contributing to progression in colorectal cancer. The results of this study provide a foundation to focus future experiments on the contribution of altered protein and phosphorylation patterns to prevention and treatment of colorectal cancer.

Study of phosphorylation events for cancer diagnoses and treatment

The activation of signaling cascades in response to extracellular and intracellular stimuli to control cell growth, proliferation and survival, is orchestrated by protein kinases via phosphorylation. A critical issue is the study of the mechanisms of cancer cells for the development of more effective drugs. With the application of the new proteomic technologies, together with the advancement in the sequencing of the human proteome, patients will therefore be benefited by the discovery of novel therapeutic and/or diagnostic protein targets. Furthermore, the advances in proteomic approaches and the Human Proteome Organization (HUPO) have opened a new door which is helpful in the identification of patients at risk and towards improving current therapies. Modification of the signaling-networks via mutations or abnormal protein expression underlies the cause or consequence of many diseases including cancer. Resulting data is used to reveal connections between genes proteins and compounds and the related molecular pathways for underlining disease states. As a delegate of HUPO, for human proteome on children assays and studies, we, at Hospital Universitario Niño Jesús, are seeking to support the human proteome in this context. Clinical goals have to be clearly established and proteomics experts have to set up the appropriate proteomic strategy, which coupled to bioinformatics will make it possible to achieve new therapies for patients with poor prognosis. We envision to combine our up-coming data to the HUPO organization in order to support international efforts to advance the cure of cancer disease.

A proteomic approach to obesity and type 2 diabetes

The incidence of obesity and type diabetes 2 has increased dramatically resulting in an increased interest in its biomedical relevance. However, the mechanisms that trigger the development of diabetes type 2 in obese patients remain largely unknown. Scientific, clinical and pharmaceutical communities are dedicating vast resources to unravel this issue by applying different omics tools. During the last decade, the advances in proteomic approaches and the Human Proteome Organization have opened and are opening a new door that may be helpful in the identification of patients at risk and to improve current therapies. Here, we briefly review some of the advances in our understanding of type 2 diabetes that have occurred through the application of proteomics. We also review, in detail, the current improvements in proteomic methodologies and new strategies that could be employed to further advance our understanding of this pathology. By applying these new proteomic advances, novel therapeutic and/or diagnostic protein targets will be discovered in the obesity/Type 2 diabetes area.

Implementation of proteomics for cancer research: past, present, and future

Cancer is the leading cause of the death, accounts for about 13% of all annual deaths worldwide. Many different fields of science are collaborating together studying cancer to improve our knowledge of this lethal disease, and find better solutions for diagnosis and treatment. Proteomics is one of the most recent and rapidly growing areas in molecular biology that helps understanding cancer from an omics data analysis point of view. The human proteome project was officially initiated in 2008. Proteomics enables the scientists to interrogate a variety of biospecimens for their protein contents and measure the concentrations of these proteins. Current necessary equipment and technologies for cancer proteomics are mass spectrometry, protein microarrays, nanotechnology and bioinformatics. In this paper, we provide a brief review on proteomics and its application in cancer research. After a brief introduction including its definition, we summarize the history of major previous work conducted by researchers, followed by an overview on the role of proteomics in cancer studies. We also provide a list of different utilities in cancer proteomics and investigate their advantages and shortcomings from theoretical and practical angles. Finally, we explore some of the main challenges and conclude the paper with future directions in this field.

Application of oncoproteomics to aberrant signalling networks in changing the treatment paradigm in acute lymphoblastic leukaemia

Oncoproteomics is an important innovation in the early diagnosis, management and development of personalized treatment of acute lymphoblastic leukaemia (ALL). As inherent factors are not completely known - e.g. age or family history, radiation exposure, benzene chemical exposure, certain viral exposures such as infection with the human T-cell lymphoma/leukaemia virus-1, as well as some inherited syndromes may raise the risk of ALL - each ALL patient may modify the susceptibility of therapy. Indeed, we consider these unknown inherent factors could be explained via coupling cytogenetics plus proteomics, especially when proteins are the ones which play function within cells. Innovative proteomics to ALL therapy may help to understand the mechanism of drug resistance and toxicities, which in turn will provide some leads to improve ALL management. Most important of these are shotgun proteomic strategies to unravel ALL aberrant signalling networks. Some shotgun proteomic innovations and bioinformatic tools for ALL therapies will be discussed. As network proteins are distinctive characteristics for ALL patients, unrevealed by cytogenetics, those network proteins are currently an important source of novel therapeutic targets that emerge from shotgun proteomics. Indeed, ALL evolution can be studied for each individual patient via oncoproteomics.

Wrangling phosphoproteomic data to elucidate cancer signaling pathways

The interpretation of biological data sets is essential for generating hypotheses that guide research, yet modern methods of global analysis challenge our ability to discern meaningful patterns and then convey results in a way that can be easily appreciated. Proteomic data is especially challenging because mass spectrometry detectors often miss peptides in complex samples, resulting in sparsely populated data sets. Using the R programming language and techniques from the field of pattern recognition, we have devised methods to resolve and evaluate clusters of proteins related by their pattern of expression in different samples in proteomic data sets. We examined tyrosine phosphoproteomic data from lung cancer samples. We calculated dissimilarities between the proteins based on Pearson or Spearman correlations and on Euclidean distances, whilst dealing with large amounts of missing data. The dissimilarities were then used as feature vectors in clustering and visualization algorithms. The quality of the clusterings and visualizations were evaluated internally based on the primary data and externally based on gene ontology and protein interaction networks. The results show that t-distributed stochastic neighbor embedding (t-SNE) followed by minimum spanning tree methods groups sparse proteomic data into meaningful clusters more effectively than other methods such as k-means and classical multidimensional scaling. Furthermore, our results show that using a combination of Spearman correlation and Euclidean distance as a dissimilarity representation increases the resolution of clusters. Our analyses show that many clusters contain one or more tyrosine kinases and include known effectors as well as proteins with no known interactions. Visualizing these clusters as networks elucidated previously unknown tyrosine kinase signal transduction pathways that drive cancer. Our approach can be applied to other data types, and can be easily adopted because open source software packages are employed.

Phosphoproteomics and lung cancer research

Massive evidence suggests that genetic abnormalities contribute to the development of lung cancer. These molecular abnormalities may serve as diagnostic, prognostic and predictive biomarkers for this deadly disease. It is imperative to search these biomarkers in different tumorigenesis pathways so as to provide the most appropriate therapy for each individual patient with lung malignancy. Phosphoproteomics is a promising technology for the identification of biomarkers and novel therapeutic targets for cancer. Thousands of proteins interact via physical and chemical association. Moreover, some proteins can covalently modify other proteins post-translationally. These post-translational modifications ultimately give rise to the emergent functions of cells in sequence, space and time. Phosphoproteomics clinical researches imply the comprehensive analysis of the proteins that are expressed in cells or tissues and can be employed at different stages. In addition, understanding the functions of phosphorylated proteins requires the study of proteomes as linked systems rather than collections of individual protein molecules. In fact, proteomics approaches coupled with affinity chromatography strategies followed by mass spectrometry have been used to elucidate relevant biological questions. This article will discuss the relevant clues of post-translational modifications, phosphorylated proteins, and useful proteomics approaches to identify molecular cancer signatures. The recent progress in phosphoproteomics research in lung cancer will be also discussed.

Functional phosphoproteomic mass spectrometry-based approaches

Mass Spectrometry (MS)-based phosphoproteomics tools are crucial for understanding the structure and dynamics of signaling networks. Approaches such as affinity purification followed by MS have also been used to elucidate relevant biological questions in health and disease.

The study of proteomes and phosphoproteomes as linked systems, rather than research studies of individual proteins, are necessary to understand the functions of phosphorylated and un-phosphorylated proteins under spatial and temporal conditions. Phosphoproteome studies also facilitate drug target protein identification which may be clinically useful in the near future.

Here, we provide an overview of general principles of signaling pathways versus phosphorylation. Likewise, we detail chemical phosphoproteomic tools, including pros and cons with examples where these methods have been applied. In addition, basic clues of electrospray ionization and collision induced dissociation fragmentation are detailed in a simple manner for successful phosphoproteomic clinical studies.

The international effort: building the bridge for Translational Medicine: Report of the 1st International Conference of Translational Medicine (ICTM)

BACKGROUND

Supported by the International Society for Translational Medicine (ISTM), Wenzhou Medical College and the First Affiliated Hospital of Wenzhou Medical College, the International Conference on Translational Medicine (ICTM) was held on October 22-23, 2011 in Wenzhou, China. Nearly 800 registrants attended the meeting, primarily representing institutes and hospitals in Europe, The United States of America, And Asia, and China. The meeting was chaired and organized by Dr. Xiangdong Wang, Xiaoming Chen, Richard Coico, Jeffrey M. Drazen, Richard Horton, Francesco M. Marincola, Laurentiu M. Popescu, Jia Qu and Aamir Shahzad.

FINDINGS

The meeting focused on the communication of the need to foster translational medicine (TM) by building and broadening bridges between basic research and clinical studies at the international level. The meeting included distinguished TM experts from academia, the pharmaceutical and diagnostics industries, government agencies, regulators, and clinicians and provided the opportunity to identify shared interests and efforts for collaborative approaches utilizing cutting edge technologies, innovative approaches and novel therapeutic interventions. The meeting defined the concept of TM in its two-way operational scheme and emphasized the need for bed to bench efforts based directly on clinical observation.

CONCLUSIONS

It was the meeting participants' realization that the shared main goals of TM include breaking the separation between clinic practice and basic research, establishing positive feedback by understanding the basis of expected and unexpected clinical outcomes and accelerating basic research relevant to human suffering. The primary objectives of the meeting were two-fold: to accelerate the two-way translation by informing the participants representing the different disciplines about the state of art activities around TM approaches; and to identify areas that need to be supported by redirecting limited resources as well as identifying new sources of funding. This report summarizes key concepts presented during the meeting representing the state-of-art translational research and salient aspects of the ensuing discussions.

Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer

OBJECTIVES

Cigarette smoking is the most demonstrated risk factor for the development of lung cancer, while the related genetic mechanisms are still unclear.

METHODS

The preprocessed microarray expression dataset was downloaded from Gene Expression Omnibus database. Samples were classified according to the disease state, stage and smoking state. A new computational strategy was applied for the identification and biological interpretation of new candidate genes in lung cancer and smoking by coupling a network-based approach with gene set enrichment analysis.

MEASUREMENTS

Network analysis was performed by pair-wise comparison according to the disease states (tumor or normal), smoking states (current smokers or nonsmokers or former smokers), or the disease stage (stages I-IV). The most activated metabolic pathways were identified by gene set enrichment analysis.

RESULTS

Panels of top ranked gene candidates in smoking or cancer development were identified, including genes involved in cell proliferation and drug metabolism like cytochrome P450 and WW domain containing transcription regulator 1. Semaphorin 5A and protein phosphatase 1F are the common genes represented as major hubs in both the smoking and cancer related network. Six pathways, e.g. cell cycle, DNA replication, RNA transport, protein processing in endoplasmic reticulum, vascular smooth muscle contraction and endocytosis were commonly involved in smoking and lung cancer when comparing the top ten selected pathways.

CONCLUSION

New approach of bioinformatics for biomarker identification and validation can probe into deep genetic relationships between cigarette smoking and lung cancer. Our studies indicate that disease-specific network biomarkers, interaction between genes/proteins, or cross-talking of pathways provide more specific values for the development of precision therapies for lung.

Effects of autoclaving and high pressure on allergenicity of hazelnut proteins

Background

Hazelnut is reported as a causative agent of allergic reactions. However it is also an edible nut with health benefits. The allergenic characteristics of hazelnut-samples after autoclaving (AC) and high-pressure (HHP) processing have been studied and are also presented here. Previous studies demonstrated that AC treatments were responsible for structural transformation of protein structure motifs. Thus, structural analyses of allergen proteins from hazelnut were carried out to observe what is occurring in relation to the specific-IgE recognition of the related allergenic proteins. The aims of this work are to evaluate the effect of AC and HHP processing on hazelnut in vitro allergenicity using human-sera and to analyse the complexity of hazelnut allergen-protein structures.

Methods

Hazelnut-samples were subjected to AC and HHP processing. The specific IgE- reactivity was studied in 15 allergic clinic-patients via western blotting analyses. A series of homology-based-bioinformatics 3D-models (Cora 1, Cora 8, Cora 9 and Cora 11) were generated for the antigens included in the study to analyse the co mplexity of their protein structure. This study is supported by the Declaration of Helsinki and subsequent ethical guidelines.

Results

A severe reduction in vitro in allergenicity to hazelnut after AC processing was observed in the allergic clinic-patients studied. The specific-IgE binding of some of the described immunoreactive hazelnut protein-bands: Cora 1 ~18KDa, Cora 8 ~9KDa, Cora 9 ~35-40KDa and Cora 11 ~47-48 KDa decreases. Furthermore a relevant glycosylation was assigned and visualized via structural analysis of proteins (3D-modelling) for the first time in the protein-allergen Cora 11 showing a new role which could open a new door for allergenicity-unravellings.

Conclusion

Hazelnut allergenicity-studies in vivo via Prick-Prick and other means using AC processing are crucial to verify the data we observed via in vitro analyses. Glycosylation studies provided us with clues to elucidate, in the near future, mechanisms of the structures that contribute to hazelnut allergenicity, which thus, in turn, help alleviate food allergens.

Relevant phosphoproteomic and mass spectrometry: approaches useful in clinical research

Background

"It's not what we do, it's the way that we do it". Never has this maxim been truer in proteomics than now. Mass Spectrometry-based proteomics/phosphoproteomics tools are critical to understand the structure and dynamics (spatial and temporal) of signalling that engages and migrates through the entire proteome. Approaches such as affinity purification followed by Mass Spectrometry (MS) have been used to elucidate relevant biological questions disease vs. health. Thousands of proteins interact via physical and chemical association. Moreover, certain proteins can covalently modify other proteins post-translationally. These post-translational modifications (PTMs) ultimately give rise to the emergent functions of cells in sequence, space and time.

Findings

Understanding the functions of phosphorylated proteins thus requires one to study proteomes as linked-systems rather than collections of individual protein molecules. Indeed, the interacting proteome or protein-network knowledge has recently received much attention, as network-systems (signalling pathways) are effective snapshots in time, of the proteome as a whole. MS approaches are clearly essential, in spite of the difficulties of some low abundance proteins for future clinical advances.

Conclusion

Clinical proteomics-MS has come a long way in the past decade in terms of technology/platform development, protein chemistry, and together with bioinformatics and other OMICS tools to identify molecular signatures of diseases based on protein pathways and signalling cascades. Hence, there is great promise for disease diagnosis, prognosis, and prediction of therapeutic outcome on an individualized basis. However, and as a general rule, without correct study design, strategy and implementation of robust analytical methodologies, the efforts, efficiency and expectations to make biomarkers (especially phosphorylated kinases) a useful reality in the near future, can easily be hampered.